Method for enhancing pulse oximetry calculations in the presence of correlated artifacts

ABSTRACT

Methods and systems for determining a physiological parameter in the presence of correlated artifact are provided. One method includes receiving two waveforms corresponding to two different wavelengths of light from a patient. Each of the two waveforms includes a correlated artifact. The method also includes combining the two waveforms to form a plurality of weighted difference waveforms, wherein the plurality of weighted difference waveforms vary from one another by a value of a multiplier. The method further includes identifying one of the weighted difference waveforms from the plurality of weighted difference waveforms using a characteristic of one or more of the plurality of weighted difference waveforms and determining a characteristic of the correlated artifact based at least in part on the identified weighted difference waveform.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/852,505, filed Mar. 28, 2013, which itself is a continuation of Ser.No. 12/143,358, filed Jun. 20, 2008 (U.S. Pat. No. 8,423,109), whichitself is a continuation of U.S. application Ser. No. 11/072,682, filedMar. 3, 2005 (U.S. Pat. No. 7,392,075), the disclosure of which ishereby incorporated by reference in its entirety for all purposes.

BACKGROUND

The present invention relates in general to pulse oximetry, and inparticular to the processing of signals generated by a pulse oximeter.

A pulse oximeter is typically used to measure various bloodcharacteristics including the blood oxygen saturation of hemoglobin inarterial blood and the pulse rate of the patient. Measurement of thesecharacteristics has been accomplished by use of a non-invasive sensorthat passes light through a portion of a patient's blood perfused tissueand photo-electrically senses the absorption and scattering of light insuch tissue. The amount of light absorbed and scattered is then used toestimate the amount of blood constituent in the tissue using variousalgorithms known in the art. The “pulse” in pulse oximetry comes fromthe time varying amount of arterial blood in the tissue during a cardiaccycle. The signal processed from the sensed optical measurement is thefamiliar plethysmographic waveform, which corresponds with the cyclicattenuation of optical energy through a portion of a patient's bloodperfused tissue.

Various physiological and/or external factors can adversely impact theaccuracy and/or the reliability of physiological parameters that areestimated by a pulse oximeter. These undesirable factors are sometimesreferred to as artifacts. Artifacts in general and correlated artifactsin particular can be caused by motion, respiratory artifact, orelectronic interference. Correlated artifact is an artifact thatperturbs more than one of the signals that are provided by an oximetersensor, and where the perturbations are largely correlated between thosesignals.

It is desirable for a pulse oximetry system to be able to perform itscalculations in the presence of correlated artifacts.

SUMMARY

The present invention provides a pulse oximeter that has the capabilityof performing calculations in the presence of correlated artifacts. Theembodiments of the present invention provide a method of combining thecorrelated artifacts from multiple signals so as to cancel or reduce theamplitude of the artifact in the combined signal, wherein the weightsfor the combining are determined by evaluation of pulse shapecharacteristics in the combined signal.

In one aspect, the present invention provides a method for determining aphysiological parameter in the presence of correlated artifact. Themethod includes obtaining two digital waveforms, x and y, the waveformsbeing representative of the absorption of two wavelengths ofelectromagnetic energy received from a blood-perfused tissue, and whereeach of the waveforms has a component corresponding to aplethysmographic waveform and a component corresponding to thecorrelated artifact; calculating several weighted difference waveformsof the form x−R*y, where R is a multiplier, by varying R over a range;evaluating the several weighted difference waveforms using a shapecharacteristic of the weighted difference waveform; identifying aweighted difference waveform most closely representative of theplethysmographic waveform; identifying a weighted difference waveformmost different from the plethysmographic waveform; determining apleth-based physiological parameter using the waveform most closelyrepresentative of the plethysmographic waveform; determining at leastone artifact-based physiological parameter using the waveform mostdifferent from the plethysmographic waveform; and rejecting otherpossible candidate values for the pleth-based physiological parameterusing the artifact-based physiological parameter.

For a fuller understanding of the nature and advantages of theembodiments of the present invention, reference should be made to thefollowing detailed description taken in conjunction with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary oximeter.

FIG. 2 is a block diagram of the signal processing architecture of apulse oximeter in accordance with one embodiment of the presentinvention.

FIG. 3 is a block diagram of the signal processing architecture of apulse oximeter in accordance with one embodiment of the presentinvention for performing calculations in the presence of correlatedartifacts.

FIG. 4 is an exemplary graph of IR and Red plethysmograph at asaturation of approximately 94 percent, shown corrupted with asinusoidal artifacts of equal magnitudes in both channels, whichartifacts would yield a saturation of approximately 80 percent.

FIG. 5 is an exemplary graph of skewness of the weighted differencesignal.

FIG. 6 is an exemplary FFT graph of IR and Red plethysmograph,reflecting a pulse rate of approximately 85 beats per minutes (“BPM”),with harmonics, and a 115 BPM sinusoid.

FIG. 7 is an exemplary frequency domain histogram of saturation (vs.pulse rate) values obtainable by one of multiple methods. The histogramreflects two potential saturation estimates, one at approximately 80percent (artifact) and the other at 95 percent (pulse).

DETAILED DESCRIPTION OF THE INVENTION

The methods and systems in accordance with the embodiments of thepresent invention are directed towards enhancing pulse oximetrycalculations in the presence of correlated artifact(s). The invention isparticularly applicable to and will be explained by reference tomeasurements of oxygen saturation of hemoglobin in arterial blood andpulse or heart rate, as in pulse oximeter monitors and pulse oximetrysensors.

A typical pulse oximeter measures two physiological parameters, percentoxygen saturation of arterial blood hemoglobin (SpO₂ or sat) and pulserate. Oxygen saturation can be estimated using various techniques. Inone common technique, the photocurrent generated by the photo-detectoris conditioned and processed to determine the ratio of modulation ratios(ratio of ratios) of the red to infrared (IR) signals. This modulationratio has been observed to correlate well to arterial oxygen saturation.Pulse oximeters and sensors may be empirically calibrated by measuringthe modulation ratio over a range of in vivo measured arterial oxygensaturations (SaO₂) on a set of patients, healthy volunteers, or animals.The observed correlation is used in an inverse manner to estimate bloodoxygen saturation (SpO₂) based on the measured value of modulationratios of a patient. The estimation of oxygen saturation usingmodulation ratios is described in U.S. Pat. No. 5,853,364, entitled“METHOD AND APPARATUS FOR ESTIMATING PHYSIOLOGICAL PARAMETERS USINGMODEL-BASED ADAPTIVE FILTERING,” issued Dec. 29, 1998, and U.S. Pat. No.4,911,167, entitled “METHOD AND APPARATUS FOR DETECTING OPTICAL PULSES,”issued Mar. 27, 1990, which are both herein incorporated by reference intheir entirety for all purposes. The relationship between oxygensaturation and modulation ratio is described, for example, in U.S. Pat.No. 5,645,059, entitled “MEDICAL SENSOR WITH MODULATED ENCODING SCHEME,”issued Jul. 8, 1997, which is herein incorporated by reference in itsentirety for all purposes. Most pulse oximeters extract theplethysmographic signal having first determined saturation or pulserate, both of which are susceptible to interference.

FIG. 1 is a block diagram of one embodiment of a pulse oximeter that maybe configured to implement the embodiments of the present invention.Light from light source 110 passes into a blood perfused tissue 112, andis scattered and detected by photodetector 114. A sensor 100 containingthe light source and photodetector may also contain an encoder 116 whichprovides signals indicative of the wavelength of light source 110 toallow the oximeter to select appropriate calibration coefficients forcalculating oxygen saturation. Encoder 116 may, for instance, be aresistor.

Sensor 100 is connected to a pulse oximeter 120. The oximeter includes amicroprocessor 122 connected to an internal bus 124. Also connected tothe bus are a RAM memory 126 and a display 128. A time processing unit(TPU) 130 provides timing control signals to light drive circuitry 132which controls when light source 110 is illuminated, and if multiplelight sources are used, the multiplexed timing for the different lightsources. TPU 130 also controls the gating-in of signals fromphotodetector 114 through an amplifier 133 and a switching circuit 134.These signals are sampled at the proper time, depending upon which ofmultiple light sources is illuminated, if multiple light sources areused. The received signal is passed through an amplifier 136, a low passfilter 138, and an analog-to-digital converter 140. The digital data isthen stored in a queued serial module (QSM) 142, for later downloadingto RAM 126 as QSM 142 fills up. In one embodiment, there may be multipleparallel paths of separate amplifier, filter and A/D converters formultiple light wavelengths or spectra received.

Based on the value of the received signals corresponding to the lightreceived by photodetector 114, microprocessor 122 will calculate theoxygen saturation using various algorithms. These algorithms requirecoefficients, which may be empirically determined, corresponding to, forexample, the wavelengths of light used. These are stored in a ROM 146.In a two-wavelength system, the particular set of coefficients chosenfor any pair of wavelength spectra is determined by the value indicatedby encoder 116 corresponding to a particular light source in aparticular sensor 100. In one embodiment, multiple resistor values maybe assigned to select different sets of coefficients. In anotherembodiment, the same resistors are used to select from among thecoefficients appropriate for an infrared source paired with either anear red source or far red source. The selection between whether thenear red or far red set will be chosen can be selected with a controlinput from control inputs 154. Control inputs 154 may be, for instance,a switch on the pulse oximeter, a keyboard, or a port providinginstructions from a remote host computer. Furthermore, any number ofmethods or algorithms may be used to determine a patient's pulse rate,oxygen saturation or any other desired physiological parameter.

The brief description of an exemplary pulse oximeter set forth above,serves as a basis for describing the methods for enhancing pulseoximetry calculation in the presence of correlated artifact, which aredescribed below.

The embodiments of the present invention may be implemented as a part ofa larger signal processing system used to process optical signals forthe purposes of operating a pulse oximeter. Such a signal processingsystem is shown in FIG. 2, which is a block diagram 200 of a signalprocessing architecture of a pulse oximeter in accordance with oneembodiment of the present invention. The signal processing architecture200 in accordance with the embodiments of the present invention may beimplemented as a software algorithm that is executed by a processor of apulse oximeter. In addition to calculating oxygen saturation and pulserate, the system 200 measures various signal metrics that are used todetermine filter weighting coefficients. Signal metrics are things thatindicate if a pulse is likely a plethysmograph or noise. Signal metricsmay be related to, for example, frequency (is it in the range of a humanheart rate), shape (is it shaped like a cardiac pulse), rise time, etc.The system shown in FIG. 2 calculates both the oxygen saturation, andthe pulse rate, as well as detecting venous pulsation and sensor off andlost pulse conditions, which are described separately below.

I. Oxygen Saturation Calculation

Block 202 represents the operation of the Signal Conditioning block. Thedigitized red and IR signals or waveforms are received and areconditioned in this block by: (1) taking the 1^(st) derivative to getrid of baseline shift, (2) low pass filtering with fixed coefficients,and (3) dividing by a DC value to preserve the ratio. The function ofthe Signal Conditioning subsystem is to emphasize the higher frequenciesthat occur in the human plethysmograph and to attenuate low frequenciesin which motion artifact is usually concentrated. The SignalConditioning subsystem selects its filter coefficients (wide or narrowband) based on hardware characteristics identified duringinitialization. Inputs to block 202 are digitized red and IR signals,and its outputs are pre-processed red and IR signals.

Block 204 represents the operation of the Pulse Identification andQualification block. The low pass filtered digitized red and IR signalsare provided to this block to identify pulses, and qualify them aslikely arterial pulses. This is done using a pre-trained neural network,and is primarily done on the IR signal. The pulse is identified byexamining its amplitude, shape and frequency. An input to this block isthe average pulse period from block 208. This function changes theupfront qualification using the pulse rate. The output of block 204indicates the degree of arrhythmia and individual pulse quality. Inputsto block 204 are: (1) pre-processed red and IR signals, (2) Averagepulse period, and (3) lowpass waveforms from the low pass filter.Outputs from block 204 include: (1) degree of arrhythmia, (2) pulseamplitude variations, (3) individual pulse quality, (4) pulse beepnotification, and (5) qualified pulse periods and age.

Block 206 is used to compute signal quality metrics. This block (block206) determines the pulse shape (e.g., derivative skew), periodvariability, pulse amplitude and variability, Ratio of Ratiosvariability, and frequency content relative to pulse rate. Inputs toblock 206 include: (1) raw digitized red and IR signals, (2) degree ofarrhythmia, individual pulse quality, pulse amplitude variation, (3)pre-processed red and IR signals, and (4) average pulse period. Outputsfrom block 206 include: (1) lowpass and ensemble averaging filterweights, (2) metrics for sensor off detector, (3) normalizedpre-processed waveforms, and (4) percent modulation.

Block 208 computes average pulse periods. This block (block 208)calculates the average pulse period from the pulses received. Inputs toblock 208 include: qualified pulse periods and age. An output from block208 includes the average pulse period.

Block 210 represents the functioning of the lowpass filter and ensembleaveraging subsystem. Block 210 low pass filters and ensemble averagesnormalized and preprocessed waveforms processed by block 206. Theweights for the low pass filter are determined by the Signal Metricsblock 206. The signal is also ensemble averaged (this attenuatesfrequencies other than those of interest near the pulse rate and itsharmonics), with the ensemble averaging filter weights also determinedby Signal Metrics block 206. Less weight is assigned if the signal isflagged as degraded. More weight is assigned if the signal is flagged asarrhythmic because ensemble-averaging is not appropriate duringarrhythmia. Red and IR waveforms are processed separately, but with thesame filtering weights. The filtering is delayed (e.g., approximatelyone second) to allow the signal metrics to be calculated first.

The filters use continuously variable weights. If samples are not to beensemble-averaged, then the weighting for the previous filtered samplesis set to zero in the weighted average, and the new samples are stillprocessed through the signal processing algorithm. This block tracks theage of the signal and/or the accumulated amount of filtering (e.g., sumof response times and delays in processing). Too old a result will beflagged, if good pulses haven't been detected for a while. The inputs toblock 210 include: (1) normalized pre-processed red and IR signals, (2)average pulse period, (3) low pass filter weights and ensemble averagingfilter weights, (4) ECG triggers, if available, and (5) IR fundamental,for zero-crossing triggers. Outputs from block 210 include: (1) filteredred and IR signals, and (2) age.

Block 212 represents operations that estimate the ratio-of-ratiosvariance for the filtered waveforms and calculate averaging weights. Thevariable weighting for the filter is controlled by the ratio-of-ratiosvariance. The effect of this variable-weight filtering is that theratio-of-ratios changes slowly as artifact increases and changes quicklyas artifact decreases. The subsystem has two response modes, includingfast and normal modes. For example, filtering in the fast mode targetsan age metric of 3 seconds, and the target age may be 5 seconds in thenormal mode. In the fast mode, the minimum weighting of the currentvalue is clipped at a higher level. In other words, a low weight isassigned to the newest ratio-of-ratios calculation if there is noisepresent, and a high weight if no noise is present. The inputs to block212 include: (1) filtered red and IR signals and age, (2) calibrationcoefficients, and (3) response mode (e.g., user speed settings). Outputsfrom block 212 include an averaging weight for ratio-of-ratioscalculation. The averaging weight is used as an input to block 214 alongwith filtered IR and Red waveforms to calculate averaged ratio of ratiosand age.

Block 216 represents operations that calculate oxygen saturation.Saturation is calculated using an algorithm with the calibrationcoefficients and averaged ratio of ratios. Inputs to block 116 include:(1) Averaged Ratio-of-Ratios, and (2) calibration coefficients. Anoutput from block 216 is the oxygen saturation value.

II. Pulse Rate Calculation

Block 218 low pass filters and ensemble averages the signal(s)conditioned by block 202, for the pulse rate identification. The weightsfor the low pass filter are determined by the Signal Metrics block 206.The signal is also ensemble averaged (this attenuates frequencies otherthan those of interest near the pulse rate and its harmonics), with theensemble averaging filter weights also determined by Signal Metricsblock 206. Less weight is assigned if the signal is flagged as degraded.More weight is assigned if the signal is flagged as arrhythmic becauseensemble-averaging is not appropriate during arrhythmia. Red and IR areprocessed separately, but with the same filtering weights. The filteringis delayed (e.g., approximately one second) to allow the signal metricsto be calculated first.

The filters use continuously variable weights. If samples are not to beensemble-averaged, then the weighting for the previous filtered samplesis set to zero in the weighted average, and the new samples are stillprocessed through the signal processing algorithm. This block (block218) tracks the age of the signal and/or the accumulated amount offiltering (sum of response times and delays in processing). Too old aresult will be flagged (if good pulses haven't been detected forawhile). Inputs to block 218 include: (1) pre-processed red and IRsignals, (2) average pulse period, (3) lowpass filter weights andensemble averaging filter weights, (4) ECG triggers, if available, and(5) IR fundamental, for zero-crossing triggers. Outputs from block 218include: (1) filtered red and IR signals and (2) age.

Block 220, or the Filtered Pulse Identification and Qualification block,calculates the pulse periods from the filtered waveforms, and itsresults are used only when a pulse is disqualified by block 204. Inputsto block 220 include: (1) filtered red and IR signals and age, (2)average pulse period, (3) front end ID or noise floor, (4) and the kindor type of sensor that is used to detect the IR and Red energies. Outputfrom block 220 includes qualified pulse periods and age.

Block 222, or the Average Pulse Periods and Calculate Pulse Rate block,calculates the pulse rate and average pulse period. This block (block222) receives qualified pulse periods and age as inputs and provides (1)average pulse period and (2) pulse rate as outputs.

III. Venous Pulsation

Block 224, or the Detect Venous Pulsation block receives as inputs thepre-processed red and IR signals and age from Block 202, and pulse rateand provides an indication of venous pulsation as an output. Block 224also provides an IR fundamental waveform in the time domain using asingle-tooth comb filter which is output to the Ensemble Averagingfilters (e.g., block 210 and 218). Inputs to block 224 include: (1)filtered red and IR signals and age and (2) pulse rate. Outputs fromblock 124 include: an indication of venous pulsation and IR fundamental.In one embodiment, block 224 measures the “openness” of an IR-RedLissajous plot to determine the whether a flag (e.g., Venous_Pulsation)should be set. The output flag (e.g., Venous_Pulsation) is updatedperiodically (e.g., every second). In addition, the IR fundamentalwaveform is output to the Ensemble Averaging filters.

IV. Sensor Off

Block 226, or the Detect Sensor-Off and Loss of Pulse Amplitude block,uses a pre-trained neural net to determine whether the sensor is off thesurface of the blood-perfused tissue, for example, of a patient. Theinputs to the neural net are metrics that quantify several aspects ofthe behavior of the IR and Red values over the last several seconds.Samples are ignored by many of the system 200's subsystems while thesignal state is either not indicative of a pulse being present, orindicative that a sensor is not on a monitoring site (e.g., PulsePresent, Disconnect, Pulse Lost, Sensor May be Off, and Sensor Off).Inputs to block 226 include: (1) signal quality metrics, and (2) theoximeter's LED brightness, amplifier gain, and (3) an ID indicating theoximeter's hardware configuration. Outputs from block 226 include asignal state including sensor-off indication.

In the architecture 200 described above, the function of block 226,Pulse lost and Pulse Search indications, may be derived usinginformation from several parts of the signal processing architecture. Inaddition, the signal processing architecture will not use the receivedIR and red waveforms to compute oxygen saturation or pulse rate if avalid sensor is not connected, or if the Sensor-Off or Loss of PulseAmplitude are detected by the signal processing architecture.

The brief description of an embodiment of a pulse oximeter signalprocessing architecture in accordance with the present invention, setforth above, serves as a basis for describing the enhanced pulseoximetry calculations in the presence of correlated artifact(s), as isgenerally depicted by blocks 216 and 222 above.

FIG. 3 is a block diagram 300 of the signal processing architecture of apulse oximeter in accordance with one embodiment of the presentinvention for performing enhanced saturation and/or pulse ratecalculations in the presence of correlated artifacts. It should berealized that the operations depicted by the diagram 300 need not becarried out in the order shown in FIG. 3. A person skilled in the art ofpulse oximetry signal processing will realize that the operations ofFIG. 3 may be carried out in any order, or that steps may be combined,or even skipped. These various permutations of the operation inaccordance with the diagram of FIG. 3 are also within the scope of thepresent methodology. In general, the block diagram 300 shows that inblock 302, two digitized waveforms X and Y are received. In block 304,the two waveforms are combined to form a weighted difference waveformhaving the form X−R*Y, where R is a multiplier. The value of R is variedand thus a series of weighted difference waveforms are formed. In block306, the combined waveforms are evaluated using a characteristic of thepulse shape. For example, one such pulse shape characteristic is theskew of the combined waveform. In blocks 308 and 310, the combinedwaveform most closely representative of a plethysmograph and thewaveform least representative of a plethysmograph are identified. Havingidentified these two waveforms (i.e., most and least representative),the most representative waveforms is used to determine pulse rate and/oroxygen saturation (block 312). Also, the least representative waveformmay optionally be used to determine an artifact-based pulse rate and/oroxygen saturation (block 314), and the artifact-based estimates of pulserate and/or oxygen saturation may optionally be used to disregard otherpossible estimates of pulse rate and/or oxygen saturation (block 316).

The operation of diagram 300 is described in further detail below. Themethod for enhancing saturation and/or pulse rate calculation in thepresence of correlated artifact, includes the following steps, namely:

1. Calculating two or more digital waveforms, where the two waveformscorrespond to the absorption of two or more wavelengths ofelectromagnetic energy received from a pulsatile tissue bed;

2. Filtering the waveforms to emphasize one or more characteristic ofthe pulse shape that differentiates the waveform from correlatedartifact. For example, applying a first difference filter to a normal ortypical human plethysmograph produces a filtered waveform with askewness between −1 and −2 in most subjects, whereas applying the samefilter to a motion artifact signal produces a filtered waveform with anear-zero skewness.

3. Calculating multiple weighted differences between the filteredwaveforms, X and Y, of the form X−R*Y.

4. Varying R over a range such that some value of R results in aweighted difference waveform that minimizes the correlated artifact.

5. Calculating the skewness or other shape characteristic of theweighted difference waveforms over an appropriate time interval (e.g.,at least one pulse period).

6. Selecting the value of R that produces a weighted difference waveformhaving a shape that is least characteristic of a pulse. This waveformmost closely resembles the artifact.

7. Calculating a saturation value using the waveform of “6” above.

8. Using the saturation value of “7” above for selecting from amongmultiple saturation estimates calculated by other saturation calculationalgorithms.

9. Calculating saturation from the two or more filtered waveforms, butexcluding those components contained in the weighted difference waveformhaving a shape that is least characteristic of a pulse. The exclusionoperation may be performed in various ways. For example, the strongestone or more frequencies contained in the least characteristic weighteddifference waveform may be the excluded frequencies. Alternatively, theleast characteristic waveform (i.e. per “6” above) may be canceled fromthe two or more filtered waveforms using a cancellation filter.

10. Selecting the weighted difference waveform of “6” above having askew that is most characteristic of a pulse.

11. Using the weighted difference waveform of “10” above to calculate apulse rate.

12. Calculating oxygen saturation using the waveform of “10” above andusing only those components of the waveform used for calculating thepulse rate. For example, the useful components may be isolated in amanner similar to the exclusion operation of “9” above.

13. Calculating the skewness or other shape characteristic of theweighted difference waveforms over an appropriate time interval (e.g.,at least one pulse period), wherein the total absolute difference inskewness between multiple consecutive values over a selected range of Ris used as a measure of the complexity of the pulse oximetry signal,where overly complex signals are rejected as unsuitable for oxygensaturation and/or pulse rate calculation.

14. Combining “13” above, and wherein the measure of complexity isapplied to other metrics, such as for example, energy, corresponding tothe selected range of R, where overly complex signals are rejected asunsuitable for oxygen saturation and/or pulse rate calculation.

The operation of the enhanced signal processing in accordance with theembodiments of the present invention, which is generally described inconjunction with FIG. 3 above, is further described below in conjunctionwith FIGS. 4-7.

FIG. 4 is an exemplary graph of IR and Red plethysmograph at asaturation of approximately 94 percent, shown corrupted with asinusoidal artifacts of equal magnitudes in both channels, whichartifacts would yield a saturation of approximately 80 percent.

FIG. 5 is an exemplary graph of skewness of the weighted differencesignal.

FIG. 6 is an exemplary FFT graph of IR and Red plethysmograph,reflecting a pulse rate of approximately 85 beats per minutes (“BPM”),with harmonics, and a 115 BPM sinusoid.

FIG. 7 is an exemplary frequency domain histogram of saturation (vs.pulse rate) values obtainable by one of multiple methods. The histogramreflects two potential saturation estimates, one at approximately 80percent (artifact) and the other at 95 percent (pulse).

In FIG. 5, the weighted difference waveform (Red—R*IR) having a skewnessleast-characteristic of a pulse (i.e. zero skewness) occurs for R ofapproximately 0.7, or a saturation value of approximately 92 percent,which corresponds with the saturation estimate that is made using themethod of step 7 above. In step 8, this value of R is used to select thesaturation value of 94 percent from the saturation histogram of FIG. 7,and not the possible saturation value of 80 percent (from the saturationhistogram of FIG. 7). Using the complexity metrics described in step 13(i.e., the skewness graph of FIG. 5) and step 14 (for the saturationhistogram of FIG. 7) both indicate that the IR and Red waveforms aresuitable for pulse oximetry calculations, containing only two primarycomponents, of which only one has a characteristic pulse shape.

In FIG. 5, the weighted difference waveform (Red—R*IR) having a skewnessmost-characteristic of a pulse (i.e., a very non-zero skewness) occursfor R of approximately 0.1, which resulting weighted difference waveformis the inversion of the plethysmograph, and no artifact. This mostcharacteristic waveform may be used to reliably calculate pulse rate in“11,” above. In addition, a filter may be used to extract the componentsof the most-characteristic waveform from the two or more waveforms of“2” above, for use in saturation calculation per “12” above.

In some embodiments of the present invention, pulse shape metrics otherthan the skewness may be used for the processing of the waveforms. Theseother metrics include various signal quality metrics described above inconjunction with Block 206 of FIG. 2. In particular, other pulse shapemetrics may include: the pulse shape (e.g., derivative skew), periodvariability, pulse amplitude and variability, Ratio of Ratiosvariability, and frequency content relative to pulse rate, degree ofarrhythmia, individual pulse quality, pulse amplitude variation, thedegree of similarity or correlation between the two waveforms, thedegree of motion artifact by obtaining a ratio of a current pulseamplitude to the long-term average pulse amplitude of said signals, aratio of a current pulse amplitude to the previous pulse amplitude, anda ratio of a current pulse period to that of an average pulse period. Inaddition, other pulse shape metrics such as, the “MIN-MAX-MIN” and the“PATH LENGTH” pulse shape indicators, may also be used.

The “MIN-MAX-MIN” indicator provides for a measure of the arterial pulseshape. The arterial pulse referred to herein is caused by a cardiaccycle commonly referred to a heartbeat. During a typical cardiac cycle,blood pressure rises from a minimum value (MIN) at diastole to a maximum(MAX) at systole. The “MIN-MAX-MIN” indicator is a ratio represented bya fraction having the time it takes the pulse to go from a MAX to a MINas the numerator and having the time it takes the pulse to go from a MINto a MAX as the denominator. This indicator provides an indication ofthe ratio of fall to rise times of arterial pulse. A fundamental fact ofhuman physiology is that in a typical arterial pulse, it takes a shortertime to go from the diastole to systole (MIN to MAX) than it does to gofrom systole to diastole (MAX to MIN). Recognizing this fundamentalphysiological aspect, then if the “MIN-MAX-MIN” indicator shows that fora pulse, the rise time is bigger than the fall time, then this indicatesthat the sensor's light is being modulated by other than an arterialpulse. The inventor herein has identified that when a pulse's rise timeis bigger than its fall time, the light is not modulated by pulsation ofevenly distributed arterial blood, but it is most likely that theobserved pulse-like phenomenon is due to throbbing of underlying largeblood vessels or physical movement of the sensor. It is known thateither of these mechanisms may cause large errors in the calibration ofthe resulting oxygen saturation estimate. Therefore, by analyzing theshape of the arterial pulse, the “MIN-MAX-MIN” indicator determineswhether the light modulation is due to a pulsation, or evenlydistributed arterial blood, or other phenomenon such as motion.

The “PATH LENGTH” indicator is also indicative of the pulse shape. Thisindicator provides for a measure of the frequency content of the pulsewaveform relative to the pulse rate. While many algorithms may be usedto compute “PATH LENGTH,” one equation that may be used to compute it isas follows:

${PathLength} = \frac{\sum\limits_{i = 0}^{i = {{{{Samples}\_ {in}}{\_ {Pulse}}} - 1}}{\; {{IR}_{t - i} - {IR}_{t - i - 1}}}}{{Pulse\_ Max} - {Pulse\_ Min}}$

High values of this metric indicate that a high proportion of the energyin the pulse is at frequencies higher than the pulse rate. Highfrequency components in the arterial pulse shape are an indication thatlight is being modulated by other than arterial pulsations. These highfrequency components are also most likely to be caused by movement ofthe sensor. As described above, it is known that physical movement is asource of error when estimating blood oxygen saturation in pulseoximeters. Therefore, the “PATH LENGTH” indicator, is also a motionand/or pulse shape indicator, which is used to infer that signals thathave high frequency components often lead to inaccurate estimates ofpulse rate and/or blood oxygen saturation.

Accordingly, as will be understood by those of skill in the art, thepresent invention which is related to enhancing pulse oximetrycalculations in the presence of correlated artifact(s), may be embodiedin other specific forms without departing from the essentialcharacteristics thereof. For example, while some aspects of the presentembodiments have been described in the time-domain, frequency-basedmethods are equally relevant to the embodiments of the presentinvention. Accordingly, the foregoing disclosure is intended to beillustrative, but not limiting, of the scope of the invention, which isset forth in the following claims.

What is claimed is:
 1. A medical monitor, comprising: a light driveconfigured to drive one or more light emitters; circuitry configured toreceive signals from one or more photodetectors, wherein the signalscomprise a correlated artifact; a memory storing instructions configuredto: combine the plurality of received signals to form a plurality ofweighted difference waveforms; identify a weighted difference waveformfrom the plurality of weighted difference waveforms that most or leastclosely resembles a plethysmographic waveform; and determine aphysiological parameter based at least in part on the identifiedweighted difference waveform; and a processor configured to execute theinstructions.
 2. The medical monitor of claim 1, wherein the memorystores instructions configured to identify a weighted differencewaveforms from the plurality of weighted difference waveforms byidentifying the waveform that least closely resembles a plethysmographicwaveform.
 3. The medical monitor of claim 2, wherein the memory storesinstructions configured to determine a least representativephysiological parameter based at least in part on the waveform thatleast closely resembles a plethysmographic waveform.
 4. The medicalmonitor of claim 3, wherein the memory stores instructions configured toidentify physiological parameter measurements that are associated withthe correlated artifact.
 5. The medical monitor of claim 4, wherein thememory stores instructions configured to disregard physiologicalparameter measurements that are associated with the correlated artifactwhen determining the physiological parameter.
 6. The medical monitor ofclaim 1, wherein the correlated artifact comprises an artifact caused bypatient motion, a respiratory artifact, an electronic interferenceartifact, or a combination thereof.
 7. A pulse oximetry systemcomprising: a sensor configured to detect electromagnetic radiationsignals corresponding to different wavelengths of light; and a processorconfigured to: receive the signals corresponding to the differentwavelengths of light from the sensor; combine the signals to form aplurality of weighted difference waveforms that vary by a multiplier;determine a physiological parameter measurement based at least in parton the signals; and determine that the physiological parameter is validbased on its association with a waveform of the plurality of weighteddifference waveforms that most closely resembles a plethysmographicwaveform.
 8. The system of claim 7, wherein the processor is configuredto determine the physiological parameter is invalid based on itsassociation with a waveform of the plurality of weighted differencewaveforms that least closely resembles a plethysmographic waveform. 9.The system of claim 8, wherein the waveform of the plurality of weighteddifference waveforms that least closely resembles a plethysmographicwaveform has a characteristic skew associated with an artifact.
 10. Thesystem of claim 7, wherein the waveform of the plurality of weighteddifference waveforms that most closely resembles a plethysmographicwaveform has a skew associated with a pulse.
 11. The system of claim 7,wherein the waveform of the plurality of weighted difference waveformsthat most closely resembles a plethysmographic waveform has a derivativeskew associated with a pulse.
 12. The system of claim 7, wherein thewaveform of the plurality of weighted difference waveforms that mostclosely resembles a plethysmographic waveform has a pulse amplitudeassociated with a pulse.
 13. The system of claim 7, wherein the waveformof the plurality of weighted difference waveforms that most closelyresembles a plethysmographic waveform has a ratio of ratios variabilityassociated with a pulse.
 14. The system of claim 7, wherein the waveformof the plurality of weighted difference waveforms that most closelyresembles a plethysmographic waveform has a pulse amplitude variabilityassociated with a pulse.
 15. A medical monitor, comprising: a lightdrive configured to drive one or more light emitters; circuitryconfigured to receive a signal from one or more photodetectors; a memorystoring instructions configured to: determine a pulse shape of thesignal based on a min-max-min indicator, wherein the min-max-minindicator for a pulse corresponds to a time from max-min over a timefrom min-max; determine if the pulse shape is associated with a validpulse; and determine a physiological parameter measurement based atleast in part on the signal when the signal is associated with the validpulse; and a processor configured to execute the instructions.
 16. Themedical monitor of claim 15, wherein the pulse shape is not associatedwith a valid pulse when a pulse rise time is greater than a pulse falltime such that the min-max-min indicator is less than
 1. 17. The medicalmonitor of claim 15, wherein the pulse shape is associated with a validpulse when a pulse rise time is less than a pulse fall time such thatthe min-max-min indicator is greater than
 1. 18. The medical monitor ofclaim 15, wherein the memory stores instructions configured to identifyphysiological parameter measurements that are associated with thecorrelated artifact.
 19. The medical monitor of claim 15, wherein thememory stores instructions configured to disregard the signal when thesignal is not associated with the valid pulse.
 20. The medical monitorof claim 3, wherein the memory stores instructions configured toidentify physiological parameter measurements that are associated withthe correlated artifact.